Chemical Composition of Wood Chips and Wood Pellets
Bibliographic record
Abstract
The chemical composition of 23 wood chip samples and\n132 wood pellet samples manufactured in the United States and Canada\nwere analyzed for their energy and chemical properties and compared\nto German standards for pellet quality. The pellet samples obtained\nfrom various locations across northern New York and New England included\n100 different manufacturers and duplicate samples of some brands.\nThe calorific value, moisture content, and ash content of the samples\nwere determined according to the American Society for Testing and\nMaterials (ASTM) methods. Sulfate and chloride samples were prepared\nusing ASTM methods and analyzed by ion chromatography (IC). The elemental\ncompositions of the ashed wood samples were determined using inductively\ncoupled plasma mass spectrometry (ICP–MS). Mercury was measured\nby direct analysis of wood samples. The distributions of the sample\ncharacteristics, such as heating value, ash content, moisture content,\nions, and heavy elements, are presented. Major ash-forming elements\nwere Ca, K, Al, Mg, and Fe. Although heavy elements are found naturally\nin wood and bark, some pellet samples had unusually high concentrations\nof heavy elements. This contamination was likely because of inclusion\nof extraneous materials, such as scrap or painted wood, bark or leaves,\nand other possible contaminants, during pellet manufacturing processes.\nMost of the commercially available wood pellets of this study would\nmeet German and European industrial standards. However, standards\nfor elemental compositions of commercial wood pellets and chips need\nto be established in the United States to exclude extraneous materials.\nThe promulgation of such standards would reduce environmental problems\nrelated to air emissions and ash used as fertilizers for agriculture\nsoils, where there are limits on the allowable concentrations for\nmany elements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".